Evaluación de diferentes métodos de modelización para la estimación de la altura total del árbol de la Región Mediterránea de Turquía

Efficient management of timber resources and wood utilization practices require accurate and versatile information about important characteristics of forest resources for evaluating the numerous management and utilization alternatives for timber resources. Tree height is considered one of the most useful variables along with stocking and diameter at breast height, in estimating forest stand wood volumes and productivity. Six nonlinear growth functions were fitted to tree height-diameter data of three major tree species in Western Mediterranean Region’s forests of Turkey. The generalized regression neural network (GRNN) technique has been applied for tree height prediction, as well, due to its ability to fit complex nonlinear models. The performance of the models was compared and evaluated. Further, equivalence tests of the selected models were conducted. Validation showed the appropriatness of all models to predict tree height. According to the model performance criteria, the six nonlinear growth functions were able to capture the height-diameter relationships and fitted the data almost equally well, while the constructed generalized regression neural network (GRNN) models were found to be superior to all nonlinear regression models, in terms of their predictive ability. La gestión eficiente de los recursos forestales y la de utilización de la madera requieren de información precisa y versátil acerca de las características importantes de los recursos forestales para la evaluación de la gestión y de las alternativas de utilización de los recursos forestales. La altura del árbol es considerada como una de las variables más útiles, junto con la densidad, y el diámetro a la altura del pecho, en la estimación de volúmenes de madera y la productividad de masas forestales. Se ajustaron seis modelos de altura total-diámetro y se compararon con el fin de estimar con precisión la altura total del árbol de las tres principales especies de árboles en los bosques de la Región Occidental Mediterráneo de Turquía. La regresión generalizada de redes neuronales (GRNN) se presenta como una técnica alternativa de red neuronal a la técnica de regresión no lineal para estimar la altura total de los árboles debido a su capacidad para adaptarse a modelos complejos no lineales. Se compararon y evaluaron los modelos. Se llevaron a cabo otras pruebas, como la equivalencia de los modelos seleccionados. De acuerdo con los criterios del rendimiento de los modelos, las seis funciones no lineales de crecimiento fueron capaces de capturar las relaciones altura-diámetro y ajustaron los datos casi igual de bien, mientras que las construidas mediante modelos de regresión generalizados de redes neuronales (GRNN) resultaron ser superiores a todos los modelos de regresión no lineal, en términos de su capacidad predictiva

A time delay artificial neural network approach for flow routing in a river system

International audienceRiver flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN) models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in Northern Greece. The networks are time lagged feed-formatted with delayed memory processing elements at the input layer. The network topology is using multiple inputs, which include the time lagged daily flow values further up at Siatista station on the Aliakmon river and at Grevena station on the Venetikos river, which is a tributary to the Aliakmon river and a single output, which are the daily flow values at Ilarionas station. The choice of the input variables introduced to the input layer was based on the cross-correlation. The use of cross-correlation between the ith input series and the output provides a short cut to the problem of the delayed memory determination. Kalman's learning rule was used to modify the artificial neural network weights. The networks are designed by putting weights between neurons, by using the hyperbolic-tangent function for training. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The results show a good performance of the TDANN approach for forecasting the daily flow values, at Ilarionas station and demonstrate its adequacy and potential for river flow routing. The TDANN approach introduced in this study is sufficiently general and has great potential to be applicable to many hydrological and environmental applications

Long-Baseline Neutrino Facility (LBNF) and Deep Underground Neutrino Experiment (DUNE) Conceptual Design Report Volume 2: The Physics Program for DUNE at LBNF

The Physics Program for the Deep Underground Neutrino Experiment (DUNE) at the Fermilab Long-Baseline Neutrino Facility (LBNF) is described

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum